Sr. Applied Scientist, Amazon Music Search Science

Amazon Music is an immersive audio entertainment service that deepens connections between fans artists and creators. From personalized music playlists to exclusive podcasts concert livestreams to artist merch Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode and top ad-free podcasts included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited on-demand access to 100 million songs including millions in HD Ultra HD and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans artists and creators on a global scale.

Amazon Music Search Science team is seeking an experienced Applied Scientist who will join a team of experts in the field of machine learning and work together to break new ground in the world of understanding and classifying different forms of music and creating interactive experiences to help users find the music they are in the mood for. We work on machine learning problems for music classification recommender systems dialogue systems NLP and music information retrieval. Youll work in a collaborative environment where you can pursue applied research with many peta-bytes of data work on problems that havent been solved before quickly implement and deploy your algorithmic ideas at scale understand whether they succeed via statistically relevant experiments across millions of customers and publish your research. Youll see the work you do directly improve the experience of Amazon Music customers on Alexa/Echo mobile and web.








Key job responsibilities
- Use machine learning deep learning LLMs and Agentic AI techniques to create scalable solutions for business problems
- Analyze and extract relevant information from large amounts of Amazons data to help automate and optimize key processes
- Design development and evaluation of AI models for predictive learning
- Work closely with software engineering teams to drive model implementations and new feature creations
- Establish scalable efficient automated processes for large scale data analyses model development model validation and model implementation
- Research and implement novel machine learning and statistical approaches

About the team
Everyone on our team has a meaningful impact on product features new directions in music streaming and customer engagement. We are looking for new team members across a variety of job functions including software engineering/development marketing design ops and more. Come join us as we make history by launching exciting new projects in the coming team is focused on building a personalized curated and seamless music experience. We want to help our customers discover up-and-coming artists while also having access to their favorite established musicians. We build systems that are distributed on a large scale spanning our music apps web player and voice-forward audio engagement on mobile and Amazon Echo devices powered by Alexa to support our customer base. Amazon Music offerings are available in countries around the world and our applications support our mission of delivering music to customers in new and exciting ways that enhance their day-to-day lives.

- 3 years of building machine learning models for business application experience
- PhD or Masters degree and 6 years of applied research experience
- Experience programming in Java C Python or related language
- Experience with neural deep learning methods and machine learning

- Experience with modeling tools such as R scikit-learn Spark MLLib MxNet Tensorflow numpy scipy etc.
- Experience with large scale distributed systems such as Hadoop Spark etc.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees supervisors and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees supervisors and staff to ensure exceptional customer service; and follow all federal state and local laws and Company policies. Criminal history may have a direct adverse and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above as well as the abilities to adhere to company policies exercise sound judgment effectively manage stress and work safely and respectfully with others exhibit trustworthiness and professionalism and safeguard business operations and the Companys reputation. Pursuant to the Los Angeles County Fair Chance Ordinance we will consider for employment qualified applicants with arrest and conviction records.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience qualifications and location. Amazon also offers comprehensive benefits including health insurance (medical dental vision prescription Basic Life & AD&D insurance and option for Supplemental life plans EAP Mental Health Support Medical Advice Line Flexible Spending Accounts Adoption and Surrogacy Reimbursement coverage) 401(k) matching paid time off and parental leave. Learn more about our benefits at CA Sunnyvale - 192200.00 - 260000.00 USD annually